Why Use Hyperspectral Imagery


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Introduction

While multispectral images have

been in regular use since the 1970s,

the widespread use of hyperspectral

images is a relatively recent trend.

Hyperspectral imaging, also known as im-

aging spectrometry, is now a reasonably fa-

Wmiliar concept in the world of remote sensing.

However, for many remote sensing specialists who

have not yet had the opportunity to use hyperspectral imagery in

their work, the benefits of hyperspectral imagery may still be vague.

Through this article, I hope your interest in this promising technol-

ogy will be sparked as you learn about the fascinating detail available

in hyperspectral imagery; detailed information that is being har-

vested by an increasing number of investigators. Their stories will
likely persuade you that hyperspectral imagery is another power
tool that belongs in your own remote sensing toolbox.



What is Hyperspectral Imagery?


Hyperspectral images are spectrally overdetermined; they provide

ample spectral information to identify and distinguish between spec-

trally similar (but unique) materials. Consequently, hyperspectral

imagery provides the potential for more accurate and detailed infor-

mation extraction than is possible with other types of remotely

sensed data.

Most multispectral imagers (e.g. Landsat, SPOT, AVHRR) measure

reflectance of Earth’s surface material at a few wide wavelength

bands separated by spectral segments where no measurements are

taken. In contrast, most hyperspectral sensors measure

reflected radiation as a series of narrow and contiguous
wavelength bands. When the spectrum for a single pixel in
hyperspectral imagery is displayed (Figure 1), it appears
much like a spectrum measured in a spectroscopy labora-
tory. This type of detailed pixel spectrum can provide much
more information about a surface than is available in a tra-
ditional multispectral pixel spectrum.


How is Hyperspectral Imagery Analyzed?

Standard multispectral image processing techniques were generally

developed to classify multispectral images into broad categories of
surficial material or surface condition. Hyperspectral imagery pro-
vides an opportunity for more detailed image analysis. To fulfill this

potential, new image processing techniques have been developed.

Boardman (1993) and Boardman et al. (1995) were among the first to

develop and commercialize a sequence of algorithms specifically

designed to extract detailed information from hyperspectral imag-

ery. These tools, applicable to a variety of applications, distinguish

and identify the unique materials present in the scene and map them

throughout the image. They remain the most widely used image

analysis tools for working with hyperspectral imagery.



Special Issues When Working With Hyperspectral Imagery

Although the potential of hyperspectral remote sensing is exciting, there are special issues that arise with this unique type of imagery. For example, many hyperspectral analysis algorithms require accu-rate atmospheric corrections to be performed. To meet this need, sophisticated atmospheric correction algorithms have been devel-oped to calculate concentrations of atmospheric gases directly from the detailed spectral information contained in the imagery itself without additional ancillary data. These corrections can be performed separately for each pixel because each pixel has a detailed spectrum associated with it. Several of these atmospheric correction algo-rithms are available within commercial image processing software. However, several image analysis algorithms have been successfully used with uncorrected imagery. For example, the BandMax tool owned by the Galileo Group has been widely used with radiance imagery.